# TODO: 导入必要的库和模块
import numpy as np
from tqdm import tqdm
import time
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
import joblib
import matplotlib.pyplot as plt

# TODO: 加载数字数据集
digits = datasets.load_digits()

# 提取特征和标签
X = digits.data
y = digits.target

# TODO: 将数据集划分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# TODO: 初始化变量以存储最佳准确率，相应的k值和最佳knn模型
best_accuracy = 0
best_k = 0
best_knn_model = None

# TODO: 初始化一个列表以存储每个k值的准确率
accuracies = []

# TODO: 尝试从1到40的k值，对于每个k值，训练knn模型，保存最佳准确率，k值和knn模型
for k in tqdm(range(1, 41), desc="进度: "):
    knn = KNeighborsClassifier(n_neighbors=k)
    knn.fit(X_train, y_train)
    predictions = knn.predict(X_test)
    accuracy = accuracy_score(y_test, predictions)

    accuracies.append(accuracy)

    if accuracy > best_accuracy:
        best_accuracy = accuracy
        best_k = k
        best_knn_model = knn
    time.sleep(0.1)

k_values = [x for x in range(1, 41)]

# 绘制图形
plt.figure(figsize=(10, 6))
plt.plot(k_values, accuracies, marker='o', linestyle='-', color='b', label='Accuracy')
plt.axvline(best_k, color='r', linestyle='--', label=f'Best k={best_k}')
plt.text(best_k, best_accuracy, f' k={best_k}, Accuracy={best_accuracy:.4f}', fontsize=12, verticalalignment='bottom')

# 添加标题和坐标轴标签
plt.title('Accuracy of different k values')
plt.xlabel('k value')
plt.ylabel('Accuracy')

# 添加图例
plt.legend()

# 显示网格
plt.grid(True)

plt.savefig('accuracy_plot.pdf', dpi=600)
plt.show()

# 将最佳KNN模型保存到二进制文件
joblib.dump(best_knn_model, 'best_knn_model.pkl')

# 打印最佳准确率和相应的k值
print(f"Best Accuracy: {best_accuracy}")
print(f"Best K value: {best_k}")